Autonomous Inter-Row Platform for In-Season Fertilizer Precision
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Solution Overview
Problem
Conventional agricultural methods face inefficiencies in fertilizer application, particularly with nitrogen and phosphorus fertilizers, due to loss through gas emission, runoff, and microbial processes, leading to uneven nutrient distribution and increased costs. Additionally, existing autonomous vehicles are not designed to navigate uneven terrain or perform in-season management tasks like sampling and seeding cover crops effectively.
Innovation Solution
An autonomous vehicle platform capable of navigating between planted rows, equipped with a seeding structure, navigation module, and microprocessor, which allows for self-direction and obstacle detection, enabling selective sampling and seeding while alerting operators to issues that require intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If conventional tractor-drawn equipment is used for fertilizer application, then fertilizer can be applied during early growth stages, but the equipment cannot be used throughout the entire growing season when crops reach heights of six feet or more
Solution Approach 1:
The system divides the fertilizer application task into segments: aerial vehicles handle in-season application to tall crops, while ground-based autonomous vehicles handle inter-row application. This segmentation allows each platform to operate in its optimal environment throughout the growing season.
Solution Approach 2:
The system transitions from ground-based only application to multi-dimensional application by introducing aerial vehicles that can access tall crops from above, while ground-based autonomous vehicles operate between rows. This adds a vertical dimension to fertilizer application capability.
2Adaptability or versatility
If high boy systems or crop dusters are used to apply fertilizer to tall crops, then accessibility is improved, but the fertilizer is applied indiscriminately to the surface of the field
Solution Approach 1:
The system uses sensors to detect crop height, soil conditions, and fertilizer needs, then adjusts application rates and locations in real-time. This feedback loop ensures precise fertilizer delivery only where needed, avoiding indiscriminate surface application.
Solution Approach 2:
The system applies fertilizer with locally adapted precision: aerial vehicles target specific crop zones based on detected needs, while ground-based autonomous vehicles apply fertilizer between rows based on real-time soil and crop condition assessment, ensuring each location receives appropriate fertilizer amounts.
3Ease of operation
If fertilizer is applied in advance or uniformly across the field, then application simplicity is maintained, but nutrient loss increases through gas emission, runoff, and microbial processes
Solution Approach 1:
The system transitions from static, pre-determined fertilizer application to dynamic, real-time application. Autonomous vehicles continuously monitor crop needs and soil conditions, adjusting fertilizer application rates and timing dynamically to match actual plant requirements, thereby reducing nutrient loss.
Solution Approach 2:
The system performs preliminary assessment of soil and crop conditions using sensors before fertilizer application, allowing it to pre-identify areas needing fertilizer and apply it precisely to those locations, preventing waste through gas emission, runoff, and microbial processes.
4Reliability
If farmers over-apply fertilizer out of anxiety about insufficient nutrients, then crop growth security is improved, but fertilizer costs and environmental loss increase
Solution Approach 1:
The system continuously monitors crop health, soil nutrient levels, and growth conditions, providing real-time feedback that allows farmers to apply fertilizer precisely when and where needed. This eliminates the need for over-application while maintaining crop growth security through data-driven decision-making.
Data Source
AI summary
An autonomous vehicle platform and system for selectively performing an in-season management task in an agricultural field while self-navigating between rows of planted crops, the autonomous vehicle platform having a vehicle base with a width so dimensioned as to be insertable through the space between two rows of planted crops, the vehicle base having an in-season task management structure configured to perform various tasks, including selectively applying fertilizer, mapping growth zones and seeding cover crop within an agricultural field.


